This repo aims to provides a comprehensive list of papers about Bayesian Deep Learning, including Bayesian Neural Networks, Bayesian Optimization, Bayesian model merging, Bayesian Classifier, Bayesian Interpretation on LLMs, etc. And this repo also provides a file saves the bibtex file of all papers in this repo.
- On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction
- Uncertainty Aware Semi-Supervised Learning on Graph Data
- Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
- On Calibration of Modern Neural Networks
- ENHANCING THE RELIABILITY OF OUT-OF-DISTRIBUTION IMAGE DETECTION IN NEURAL NETWORKS
- Evidential Deep Learning to Quantify Classification Uncertainty
- Weight Uncertainty in Neural Networks
- Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
- pitfalls of in-domain uncertainty estimation and ensembling in deep learning
- Bayesian Deep Learning and a Probabilistic Perspective of Generalization
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
- An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference
- A Bayesian Federated Learning Framework With Online Laplace Approximation
- Distilling the Knowledge in a Neural Network
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
- How good is the Bayes posterior in deep neural networks really?
- EDERATED LEARNING VIA POSTERIOR AVERAGING: A NEW PERSPECTIVE AND PRACTICAL ALGORITHM
- A SCALABLE LAPLACE APPROXIMATION FOR NEURAL NETWORKS
- FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation